Use of the vertical reflectivity profile for identification of anomalous propagation

Transcription

1 Use of the vertical reflectivity profile for identification of anomalous propagation Meteorol. Appl. 8, (2001) P P Alberoni 1, T Andersson 2, P Mezzasalma 1, D B Michelson 2 and S Nanni 1 1 ARPA-Servizio Meteorologico Regionale, viale Silvani 6, Bologna, Italy 2 Swedish Meteorological and Hydrological Institute, S Norrköping, Sweden Anomalous propagation (anaprop), analogous to the upper mirage in the visual wavelengths, is still a major problem in radar meteorology. This phenomenon assumes particular importance in automatic recognition and estimation of rainfall. Anaprop echoes from terrain features such as hills and coasts often give echoes up to dbz, equivalent to heavy rain or hail in severe thunderstorms. Anaprop echoes from sea waves may be comparable in strength to those from moderate precipitation and also form similar patterns. Based on the evidence that the vertical reflectivity profile of precipitation is quite different from the anaprop profile, two methods for anaprop identification are presented. The method proposed by the Servizio Meteorologico Regionale (SMR, Italy) simply uses the operational scan procedure to discriminate between precipitation and anaprop. At the Swedish Meteorological and Hydrological Institute an ad hoc scan strategy has been developed in order to obtain much more detail of the lowest reflectivity profile. A number of statistical parameters have been used to achieve a better discrimination between precipitation, land and sea clutter. A number of case studies, representing different echo intensities and patterns, and including a case of anaprop with embedded precipitation, are presented to assess the impact of these methods. 1. Introduction To distinguish between echoes from the earth s surface and meteorological targets is one of the oldest and yet unsolved problems in radar meteorology. The surface echo returns may be caused by terrain as well as sea waves, producing so-called clutter. Clutter from the earth s surface may be divided into two categories: normal (or permanent ) and anomalous. In the standard atmosphere, the radar ray paths go away from the earth surface with a radius of curvature equal to four thirds of the earth s radius. The propagation of the rays is determined by the vertical profile of the atmospheric refractive index, which is mainly determined by the vertical profiles of air temperature and water vapour pressure. For particular weather conditions, (e.g. surface temperature inversion) rays are refracted more than the surface of the earth so that unexpected clutter echoes become visible. Such conditions are called anomalous propagation or anaprop. Over land anaprop generally appears during the night or early morning when a pronounced radiation inversion has formed. Over seas, such as the Adriatic and the Baltic, anaprop is frequent during the seasons when the sea surface temperature is much lower than the air temperature. Alberoni et al. (1998) report results of four years analysis of refractive index gradient, using 0000 and 1200 UTC radiosonde data from the same radar site near Bologna, Italy. As earlier stated, the frequency of anaprop occurrences is higher during the night than the day (61% versus 22%); the analysis also shows a seasonal cycle of anaprop, with a well-marked night maximum during the summer months. The warm season is most affected by super refraction conditions, also seen in other areas with temperate climate, such as the coastal areas of Virginia, USA (Babin, 1996). Several authors have developed techniques to identify, remove or correct anaprop echoes, based on the different behaviour of precipitation and anaprop. Ducts formed by nocturnal radiative cooling or by advection of dry air over cooler water can produce large echo areas, which appear stationary compared to precipitation echoes. For instance, each radar has its own characteristic anaprop areas, which an experienced observer can often identify. This can be utilised in spatial time continuity checks (Hogg, 1978; Smith, 1990), or by a set of fixed clutter masks, provided for different propagation situations, (Riedl, 1994). These static approaches do not work with dynamic anaprop, such as that caused by cold downdrafts from thunderstorms (Weber et al., 1993) or clutter from sea waves. It is known that clutter and precipitation have different time fluctuation statistics and Wessels & Beekhuis (1994) have used the raw pulse to pulse reflectivity variation to discriminate them, but this technique works on raw data before any integration. Moszkowicz et al. (1993) utilise an expert system based on five parameters 257

2 P P Alberoni, T Andersson, P Mezzasalma, D B Michelson and S Nanni in order to describe echo characteristics (shape, intensity and three dimensions) and to identify spurious echoes, but a large calibration data set is necessary to obtain good results. Other authors have used expert systems or artificial intelligence tools based on neural networks, (e.g. Cornelius, 1994; Da Silveira & Holt, 1998). Doppler cancellation methods have been most successful to date, especially those systems based on Fast Fourier Transforms. Power filters produce acceptable results, whereas in Pulse Pair systems with Infinite Impulse Response (IIR) filtering the choice of cut level is critical because of the risk of rejecting rainfall information when the mean Doppler velocity is close to 0 m s 1 (Koistinen, 1996). However, these Doppler methods do not work for sea clutter, since waves generally move with velocities of a few m s 1. Moreover, the maximum unambiguous range of Doppler radar is often much less, about one half of the non-doppler radar or mode. Doppler can therefore only be used for part of the non-doppler surveillance area. Furthermore, some operational limitations prevent the use of Doppler cancellation during each data acquisition; for example clutter Doppler filtering is disabled at the SMR s radar if polarimetric measurements are required. A derived parameter, the clutter to signal ratio, from Doppler clutter correction was tested by Schmid et al. (1991); they showed that normal clutter is easier to identify than anaprop, but the ease of discrimination decreases with range. They also described the different requirements for research and operational applications. The use of polarimetric data to discriminate clutter echoes was explored, amongst others, by Freni et al. (1989) using differential reflectivity, and Wilson et al. (1994) using linear differential reflectivity. An integrated and comprehensive scheme to identify and remove clutter and anaprop echoes was developed during the latest Swiss radar upgrade (Joss & Lee, 1993). More recently, Pamment & Conway (1998) have proposed a combination of several sources of evidence (such as surface observations, satellite images, detected lightning and climatology of anaprop echoes) to determine by means of Bayesian statistics if an echo is caused by anaprop. All previous methods have been developed to manage anaprop over land but have limited effectiveness with anaprop over the sea. Radar close to a coast with a free horizon towards the sea frequently experiences anaprop from sea waves and coasts during spring and summer, though anaprop also occurs during other seasons. Since conventional precipitation observations are scarce over seas, radar is an important precipitation observation system there, making knowledge of the propagation conditions even more crucial in such environments. 258 The aim of this work is to develop quick and cheap operational tools for identifying and removing anaprop echoes, including sea clutter. The methods are based on the vertical reflectivity profile, which is a very good indicator for anaprop, including sea clutter. Mainly polar volume data from non-doppler mode have been used. 2. Wave propagation The propagation of an electromagnetic radar beam in the atmosphere depends mainly on the vertical gradient of the refractive index (n) that is determined by the vertical distribution of temperature, pressure and specific humidity. Since small deviations of n in the troposphere can cause large changes in wave propagation, a derived parameter named refractivity (N) is normally defined (refer to Doviak & Zrnic`, 1993). A first-order approximation of N can be expressed as: e N = ( n 1) 10 = p + T T where N is dimensionless, T is air temperature (K), p is atmospheric pressure (hpa) and e is water vapour pressure (hpa). Since the dominant terms in the definition of N are water vapour pressure (highly stratified with altitude) and atmospheric pressure, normally N decreases with height. The vertical gradient of N is used to classify nonstandard propagation conditions as sub-refractive ( less than normal) and super-refractive ( more than normal). In super-refractive conditions (usually called anaprop or AP) a proportion of the lower part of the radar beam is bent towards the ground and spurious and unexpected clutter echoes are visible in the radar data. The worst case of AP is caused by ducting, i.e. radar microwaves are trapped close to the ground and may travel within duct layers like in a wave-guide. In terms of vertical refractivity gradient, dn/dh, subrefractive layers are characterised by values greater than 0 km 1, normal refraction conditions range from 0 to 78.7 km 1 (the standard atmosphere gradient is 39 km 1 ), AP echoes from the ground are expected when the atmospherics layers are super-refractive (dn/dh is between 79 and 157 km 1 ) or in ducting conditions (dn/dh less than or equal to 157 km 1 ). 3. Characterisation of vertical reflectivity profile In precipitation the targets (the precipitation particles) are assumed to fill the lobe (see Figure 1 for a pictorial (1)

3 Anaprop identification and removal Figure 2. Characteristic reflectivity vertical profile for anaprop (thick line) and rain (thin line) echoes. wave formations. In fact data animations, with a time resolution of the order of 10 seconds, reveal that anaprop echoes scintillate, while precipitation echoes generally move fairly steadily. Therefore the vertical reflectivity profiles of anaprop echoes should be uneven and show a large decrease of reflectivity with antenna elevation angle. Generally strong anaprop echoes are visible up to antenna elevation angles of about 2 o. Echoes at such angles should be due to the first side lobe, which is close to the main lobe and has about the same width. There are even cases when anaprop echoes are detected at much larger elevation angles. The reflectivity profiles were found to be much more irregular for anaprop echoes than for precipitation echoes (see Figure 2). Figure 1. Conceptual model of radar wave propagation during different conditions. (a) Normal propagation with precipitation. (b) Anomalous propagation without precipitation. (c) Anomalous propagation with concurrent precipitation. presentation). The vertical reflectivity profile should be smooth, slowly decreasing reflectivities with height if the phase of the targets is homogeneous, or showing a sharp increase if the radar beam intercepts the melting layer (bright band). In anaprop conditions, even at the lowest elevation angle, most of the rays of the primary lobe have incidence angles so large relative to the duct that they pass through it. However, the lowest rays become trapped and give anaprop echoes. If the antenna elevation is somewhat increased, the rays of the primary lobe may pass the duct, but instead rays from the first side lobe become trapped. The intensity of the rays trapped may thus be expected to vary with the elevation angle and the duct probably oscillates due to The foundation of our discriminating methods is the simple observation that the main feature characterising and distinguishing anaprop reflectivity profiles from those from precipitation is the vertical discontinuity of reflectivity. In other words, in rainy situations vertical reflectivity profiles should be smooth and regular; in anaprop, part of the beam is trapped in the duct and uneven profiles with a large decrease in reflectivity are expected. Such behaviour can be easily observed through a vertical slice of radar data (see Figure 3). Two methods have been developed: the SMR method developed by the Servizio Meteorologico Regionale of the Emilia-Romagna region in Italy, and the SMHI method developed by the Swedish Meteorological and Hydrological Institute (Andersson et al. 1997) The SMR Method The technique developed at SMR is operationally oriented: only data from the regular scan strategy (four contiguous PPI from 0.5 to 3.2 every 0.9, range bin 250 m) were used to discriminate echo sources. The vertical reflectivity profile is characterised by the difference between two antenna elevation angles (RD) given by: 259

4 P P Alberoni, T Andersson, P Mezzasalma, D B Michelson and S Nanni Figure 3. Radar reflectivity (dbz) vertical slice (RHI) for (a) a rain and (b) an anaprop event. RD = V 1 V 2 (dbz) (2) where V 1 and V 2 are the antenna elevation angles (e.g. V 1 at 0.5 o and V 2 at 1.4 o ). Anaprop is diagnosed when one of the following conditions is encountered: (a) RD > T 1 (b) RD > 0 and V 2 < T 2 (3) where T 1 and T 2 are empirical threshold values. Typical RD distributions for anaprop and rain are shown in Figure 4. However, there may be a few cells with low RD in a region with anaprop echoes as well as some with high RD for actual precipitation signals. That is, the distributions of these quantities for precipitation and anaprop are overlapping. Note that only the tails of the two distributions overlap in Figure 4. In order to solve this, some kind of texture analysis is necessary. A simple way is to use the previous range bin; if the previous bin was classified as being affected by anaprop then lower values of the thresholds T 1 and T 2 are required (see Table 1). These thresholds were chosen after analyses of several anaprop events. In Figure 4 the T 1 thresholds for normal and behind conditions are superposed. Table 1. Threshold values required to define a range bin as affected by anaprop: for the first range bin encountered the normal threshold is used; for other bins following along the same radar beam, a lower threshold, behind, is applied. Threshold (dbz) Normal Behind T T The SMHI method. SMHI has introduced a more detailed reflectivity profile, obtained by using a special scan strategy with very low increments at the lowest antenna elevation angles (see Table 2) and a statistical measure, the Mean Square Deviation (MSD), defined as follows: d i = V i V i+1 (4) MSD = 1 2 d i n where V i is the pixel value at elevation angle number i for given azimuth and range. d i is only computed if both V i and V i+1 give a measurable signal, that is a signal above 30 dbz, or, in other words, that there are echoes at angles number i and i+1. If none of the elevations of an azimuth/range pixel give a measurable signal this pixel is deemed echo-free and excluded from the anaprop/non-anaprop treatment. (5) The highest elevation angle used has thus the number n+1. The number n has to be determined more or less arbitrarily. After some experiments, n = 7 was selected with elevation angles ranging between 0.5 and 1.2. It is thus possible to get n values of d i for each azimuth and range. Figure 4. RD distributions (dbz) for rain (thick line) and anaprop (thin line). The two vertical lines show the values of T 1 threshold for normal and behind cases. 260 If the number of defined d i (segments) is above 0 but less than n 1, then we assign the value of 1 dbz 2 to MSD. That is, for a MSD 0 dbz 2 the profile shall be unbroken (i.e. no echo may only appear at the lowest or highest elevation angles). This condition is applied because in precipitation there should be a vertically

5 Anaprop identification and removal Table 2. Antenna elevation scan strategy used at SMHI for reflectivity profile analysis. Number Elevation (deg) unbroken echo. Echoes from thin clouds giving precipitation may get a negative MSD, as well as precipitation echoes far away from the antenna, generally 200 km or more. In the former case the precipitation is probably very light; in the latter the beam is so high that the radar only has a qualitative surveying function, and not a quantitative measuring one. From several analysed cases it has been found that in precipitation generally MSD 16 dbz 2. The main MSD distributions for different types of echoes are illustrated in Figure 5, while major characteristics are drawn in Table 3. represent any difficulties in quantitative precipitation estimation. As Figure 5 shows, the distributions of MSD for different echo types are overlapping. For an automatic classification the MSD value alone is therefore insufficient. Some sort of texture analysis must be introduced. The task is then to design a scheme that can work with the antenna elevation angle interval used ( ). To do that, two operators, one vertical and one horizontal have been used. The vertical operator is just the MSD given by equation (5). However, a more general range of elevation angles has been introduced, so n+1 is the number of elevation angles within 1.25 times the beam-width. The reason is that it should be possible to work with different antenna elevation scanning strategies. We have mostly used the eight lowest elevation angles of Table 2. It is possible that fewer, more spaced, elevation angles may work well. Figure 5. MDS distributions for precipitation, sea clutter and anaprop clutter. Table 3. Characteristic reflectivity profiles and MSD values of precipitation and anaprop. Phenomenon Reflectivity profile MSD (dbz 2 ) Precipitation Even and unbroken 0 MSD 16 Anaprop over land Uneven and/ MSD >16 or or broken MSD <0 Anaprop over sea Broken MSD <0 Precipitation distribution of MSD is narrow, has a pronounced peak at a small positive MSD value, has only a few MSD above 16 dbz 2, and only a few negative MSD (remember that by definition a negative MSD means a broken profile). Land anaprop distribution of MSD is very broad, with several broken profiles. Sea clutter is characterised by a high frequency of broken profiles (i.e. a high frequency of negative MSD) but also some smooth or uneven profiles. One exception is the faint sea clutter often visible over seas close to the radar (at ranges up to about 40 km). Such clutter may have unbroken, smooth profiles up to antenna elevation angles of about 1.5 o. The reflectivity of such sea clutter, about or below 0 dbz, is, however, so low that it does not As the horizontal operator is concerned, an area around each bin (i.e. a kernel) has been introduced. Since polar coordinates are being used, this kernel is quadratic in azimuth gates and range bins. The actual gates of SMHI s radar are 0.86 o in azimuth and 2 km in range. The area covered by each bin will vary with range. Also, the size of the kernel may be varied. Defining k as the side of the kernel, in bins, k = 9 pixels has been mostly used. The actual pixel is centred in this square. For each bin the frequency of unbroken profiles (FUP) and frequency of accepted MSD (FAMSD) in its kernel have been computed: FUP = 100 FAMSD = 100 where the parameters are defined as: up Number of unbroken profile ( n reflectivity values give a signal > 30 dbz). bp Number of broken profile ( 1 but < n reflectivity values give a signal > 30 dbz). gp Number of good profile (i.e. unbroken profile with 0 MSD < MSD lim ). and MSD lim is a critical value. up up + bp ( ) gp up + bp ( ) (6) (7) 261

6 P P Alberoni, T Andersson, P Mezzasalma, D B Michelson and S Nanni The largest possible values of FUP and FAMSD are 100, while the smallest possible values are 0. As previously mentioned, anaprop over the sea markedly differs from anaprop over land in terms of their MSD values. Therefore, using FAMSD over land and FUP over the sea is the best choice. For each quantity there is a critical value or threshold, FUP LIM and FAMSD LIM respectively. Thus if for a pixel: FUP < FUP LIM over the sea, the echo is considered as anaprop. FAMSD < FAMSD LIM over the land, the echo is considered anaprop. A land/sea mask in polar coordinates has been used for these purposes. 4. Case studies than 0.01 mm h 1. It is also important to verify the impact of SMR s algorithm on rainfall. From Figure 6, the reflectivity distributions of the sector located between 0 60 azimuth and in km range have been computed before and after the application of this algorithm. The differences between the two distributions (see Figure 7(b)) are found to occur for values less than 25 dbz. This involves no significant modifications in the rainfall patterns; moreover, they are localised at the edges of the echoes, where the vertical structure is less defined. The effects of the SMR s method are clearly shown by a comparison between the radar map before and after its application (Figures 6(a) and 6(b)) FUP filtering On 24 August 1995 at 05:15 UTC, a small but strong mesoscale cyclone approached the Gotland radar from In this section some events are described as a means of assessing the impact of these techniques on anaprop and precipitation echoes Anaprop and rain over the Apennines and the Adriatic Sea Rain and anaprop echoes are observed during the afternoon of 2 August 1995 in Figure 6(a), when a thunderstorm line approached the Apennines from the north. An anaprop area is clearly visible in the southern part of the radar image; also, due to anaprop conditions, a larger than usual area is affected by secondary lobes (radius of about 40 km). East of the radar there are sea clutter echoes. The RD distribution of sea clutter (see Figure 7(a)) is consistent with the more general distribution of Figure 4. The anaprop algorithm also identifies sea clutter with good results, since the vertical structure is quite similar to the usual anaprop one. The map in Figure 6(a), which was obtained by using the dynamic clutter cancellation scheme, gives a clear example of anaprop echoes not completely rejected by the Doppler IIR filter. The overall behaviour of the dynamic Doppler correction is quite good and strongly reduces the mean rain rate inside the anaprop area, from 9.3 mm h 1 to 0.7 mm h 1. This confirms the strong impact of this technique in an operational environment for estimating radar rainfall, but nevertheless the residual anaprop echoes may give false alarms in an operational scenario. Furthermore, this residual anaprop lies outside the permanent clutter area, so that the application of a static mask does not reduce the spurious echoes. Inside the clutter area we remove the greater part of the clutter, though still we have some residuals due to anaprop conditions. Application of our anaprop cancellation algorithm produces a drastic improvement; no data over 10 dbz (lower limit of the rainfall range) remain and the final average rain rate estimate is less 262 Figure 6. (a) Reflectivity PPI (dbz) at 0.5 elevation recorded on 2 August 1995 at 19:19 UTC after the clutter Doppler filtering. Regions with different echo sources are bounded. (b) After the SMR s algorithm application.

7 Anaprop identification and removal The original and filter radar maps are displayed in Figures 8(c) and 8(d). All anaprop echoes, but also some very distant weather echoes, are rejected. 4.4 FUP & FAMSD filtering Tremendous anaprop and sea clutter occurred east of Gotland on 23 August 1996 at 23:45 UTC. Frontal precipitation with some convective cores is also present, arranged along a north south axis. Data were collected following a scan strategy similar to that described in Table 2, so a very good description of the vertical reflectivity structure is possible. In this event the filter is used with both FUP and FAMSD thresholds. Furthermore the land/sea mask transformed to polar coordinates is used. Details on filter characteristics are found in Table 4. Figures 8(e) and 8(f) show the situation before and after the filter application. The string of echoes in the bottom-left corner has disappeared and only a few pixels on the Latvia coast are preserved while the frontal structure is still present Strong Sea clutter echo Figure 7. (a) RD distribution for sea clutter echo. (b) Differences between reflectivity distributions on the rainfall area before and after the application of the correction technique. the west. An area affected by sea clutter and anaprop is detectable to the east. Data used to test the identification and rejection scheme are taken from the commonly used operational scan strategy, i.e. ten elevation scans with normal spacing between them (about 1 ). The situation is displayed in Figure 8(a). Filter characteristics are reported in Table 4, while Figure 8(b) shows the filtered result. All echoes east of the radar are correctly identified and cancelled. The main body of the small cyclone is maintained, but some weather echoes are removed. This happens typically at the edges of the system, as in the case of the developing rainfall cores in the southern part of it. Strong echoes from the island of Gotland and from the coast of Latvia are completely removed, as well as sea clutter FAMSD filtering The use of FAMSD to identify anaprop echo is tested on 8 October 1995 at 16:15 UTC. Weak precipitation occurs to the north of the Gotland radar while weak anaprop builds up to the west. The strongest echoes come from the island of Öland and the eastern coast of Sweden. The filter characteristics used in this case are given in Table 4. On 25 August 1997 the mirages over the Baltic Sea are arranged in a manner reminiscent of a Line Echo Wave Pattern (LEWP). This is especially evident on the Pseudo-CAPPI from the Gotland radar (see Figure 8(g)). This band is not quite circular, but has a radius of about 120 km. Outside it two other bands can be found in the southern part of the Pseudo-CAPPI. It is tempting to interpret these three bands as the places where the rays, trapped in the duct, bounce off the sea surface. Two different filter configurations are used on this event (Table 4). Figure 8(h) shows the result after the use of the FUP threshold. The residual signal over the island of Öland disappeared after the application of FAMSD (not shown). 5. Strategy for the treatment of anaprop There is no perfect filter. A filter that effectively removes pixels contaminated by anaprop will certainly also remove some precipitation pixels. In most events throughout the year, there is no anaprop, and the use of a filter during these events is thus not beneficial. Some criteria must be developed to help determine when a filter should be applied. This may be done in several ways: using radar data to decide if there is anaprop using non-radar data to decide if there is anaprop 263

8 P P Alberoni, T Andersson, P Mezzasalma, D B Michelson and S Nanni Figure 8. Radar reflectivity maps. Raw data on the left, filtered data on the right. Data acquired on: (a) and (b) 24 August 1995, (c) and (d) 8 October 1995, (e) and (f) 23 August 1996 and (g) and (h) 25 August

9 Anaprop identification and removal Table 4. SMHI filter characteristics used for each considered event. Date Time Kernel MSD LIM FUP LIM FAMDS LIM 24 August : % Not used 8 October : Not used 2.5% 23 August : % over sea 15% over land 25 August : % Not used 25 August : % over sea 8% over land 5.1. Radar data to decide if anaprop is expected There are nearly always some broken or uneven reflectivity profiles. If the reflectivity profiles are extracted routinely, it is possible to introduce a critical fraction of such non-precipitation profiles, and if this fraction is exceeded, to apply the filter. This should be performed for each radar, and the surveillance area should be subdivided considering range to the antenna and physical characteristics of the surface, as land, sea and mountains Non-radar data to decide if anaprop is expected There are several ways of deciding if anaprop can be expected, with one consideration being the characteristics of the surface. Open seas have a uniform surface, with a fairly uniform temperature not subject to any marked diurnal change. Over land the diurnal range of the air temperature at the lowest levels is pronounced, and low temperature inversions, ducts, are preferably formed during late night and early morning. In principle, for both surfaces, limited area forecast soundings could be used to compute the refractivity profile and diagnose propagation conditions (Bebbington, 1998). Over the sea a simple index could consist of the differences in air sea surface temperatures. The air temperature has to be one not subject to a large diurnal variation, which is at a higher level than the synoptic air temperature measured 2 m above the surface. The temperature at, say, 925 hpa would be sufficient. Satellite data can be used to decide if there is precipitation or not. Without precipitation, possible radar echoes should be caused by anaprop or by military disturbance activities (chaff, jamming transmitters). There are, however, some difficulties: polar orbiting satellites give a limited number of observations/day; geostationary satellites have poor resolution at latitudes above 50 o ; with dense cirrus clouds it is difficult to determine if there is precipitation or not. Cold land surfaces are easily confused with clouds. 6. Conclusions Several techniques utilising the vertical profile of reflectivity to diagnose radar echoes due to anaprop have been developed and tested. About 50 case studies of MSD, using the Swedish Ericsson C-band Doppler weather radars at Gotland and Norrköping, and some case studies with RD, using the C-band GPM 500 C dual polarisation Doppler weather radar of S. Pietro Capofiume, were carried out. The following conclusions have been reached: In non-anaprop conditions precipitation is characterised by smooth, unbroken reflectivity profiles. In anaprop conditions, the anaprop echoes from land are characterised by frequent high values of RD and MSD, as well as broken profiles. However, the frequency distributions of these parameters for precipitation and anaprop overlap. Anaprop echoes from sea waves, sea clutter, have broken reflectivity profiles. During anaprop conditions with precipitation, precipitation echoes may have high MSD values. That probably occurs when beam lobes are subject to anomalous bending before reaching the precipitation area. Though anaprop and precipitation generally are not concurrent, they are not mutually exclusive. With co-located anaprop and precipitation, an echo may consist of a contribution from the disturbed part of the lobe as well as one from that normally propagated. The contribution from the disturbed part of the lobe may then be due to precipitation or the surface of the earth. In such cases the meteorological radar equation does not apply. Clearly, more research is needed in this area. Precipitation requires a fairly large vertical extent of the precipitation echo. In order to get a value of parameters such as RD and MSD, a smooth echo of a fairly large vertical extent is necessary. Echoes discarded by these methods have a low (angular) vertical extent, are broken or have an uneven vertical reflectivity profile, and are probably not due to precipitation. Possible precipitation at close ranges should be very light, at long ranges the beam (under normal propagation conditions) is so high above the surface that it is impossible to decide if there is any precipitation at the surface. 265

10 P P Alberoni, T Andersson, P Mezzasalma, D B Michelson and S Nanni At this stage these methods can be characterised as methods to diagnose anaprop. The ultimate goal to correct data contaminated by anaprop is only possible during clear-cut conditions; that is, when there is either precipitation or anaprop, not when precipitation and anaprop are concurrent. An echo can be due to both terrain features, depicted by anomalous propagation, and precipitation. Such echoes can at most be classified as anaprop by these methods. However, it must be remembered that precipitation and anaprop events are mostly exclusive. Acknowledgements This work is part of the DARTH project and is supported by the European Union, Contract ENV4- CT The authors are thankful to the two reviewers who provided helpful comments and suggestions. References Alberoni, P. P., Nanni, S., Mezzasalma, P., Bech, J., Lorente, J. & Codina, B. (1998). Dynamic suppression of anaprop conditions. In Final Report to the European Union on Contract No. ENV4-CT , DARTH Project. Andersson, T., Alberoni, P. P., Mezzasalma, P., Michelson, D. B. & Nanni, S. (1997). Anomalous propagation: identification from terrain and sea waves using vertical reflectivity profile analysis. In Preprints, 28th Conf. on Rad. Meteorol., Am. Meteorol. Soc., Boston, Babin, M. S. (1996). Surface duct height distribution for Wallops Island, Virginia, J. Appl. Meteorol., 35: Bebbington, D. (1998). Anomalous propagation modelling. In Final Report to the European Union on Contract No. ENV4-CT , DARTH Project. Cornelius, R. (1994). Detecting ground clutter contamination of weather radar data with Neural Networks. In Preprints, World Congress on Neural Networks, Int. Neural Network Soc., Annual Meeting, San Diego, California. Da Silveira, R. B. & Holt, A. R. (1998). A S-band/C-band comparison of the use of polarisation for clutter classification in weather radars using neural networks. In Proceedings of COST 75 Seminar on Advanced Weather Radar Systems, Locarno, Switzerland, March 1998, European Commission, Report EUR EN, Doviak, R. J. & Zrnic, D. S. (1993). Doppler Radar and Weather Measurements. 2nd edn., Academic Press., 458 pp. Freni, A., Giuli, D., Gherardelli, M., Minuti, S., Seliga, T. & Aydin, K. (1989). Rainfall clutter discrimination in dual linear polarization radars. In Preprints, 24th Conf. on Rad. Meteorol., Am. Meteorol. Soc., Boston, Hogg, W. D. (1978). Quality control and analysis of an archive of digital radar data. In Preprints, 18th Conf. on Rad. Meteorol., Am. Meteorol. Soc., Boston, Joss, J. & Lee, R. (1993). Scan strategy, clutter suppression, calibration and vertical profile corrections. In Preprints, 26th Conf. on Rad. Meteorol., Am. Meteorol. Soc., Boston, Koistinen, J. (1996). Clutter cancellation and the capabilities of modern Doppler radars. In Proceedings, COST-75 Workshop on Doppler Weather radar, Cervignano del Friuli (UD), Italy, October 1996, European Commission, Luxembourg: Office for Official Publications of the EC, Moszkowicz, S., Ciach G. & Krajewski, W. (1993). Evaluation of a statistical method for recognition of anomalous propagation in radar reflectivity fields. In Preprints, 26th Conf. on Rad. Meteorol., Am. Meteorol. Soc., Boston, Pamment, J. A. & Conway, B. J. (1998). Objective identification of echoes due to anomalous propagation in weather radar data. J. Atmos. Oceanic Technol., 15: Riedl, J. (1994). Examples of improvements to clutter suppression in current operational weather radar systems. In Proceedings of COST 75 Seminar on Advanced Radar Systems, Brussels, September 1994, European Commission, Report EUR EN, Schmid, W., Hogl, D., Vekoski, A. & Joss, J. (1991). Test of clutter suppression techniques in the Swiss Alps. In Preprints, 25th Conf. on Rad. Meteorol., Am. Meteorol. Soc., Boston, Smith, P. L. Jr. (1990). Precipitation measurements and hydrology: Panel report. In Radar in Meteorology, D. Atlas, Ed., Am. Meteorol. Soc., Boston, Weber, M., Stone, M. & Cullen, J. (1993). Anomalous propagation associated with thunderstorm outflows. In Preprints, 26th Conf. on Rad. Meteorol., Am. Meteorol. Soc., Boston, Wessels, H. R. & Beekhuis, J. H. (1994). Stepwise procedure for suppression of anomalous ground clutter. In Proceedings of COST 75 Seminar on Advanced Radar Systems, Brussels, September 1994, European Commission, Report EUR EN, Wilson, D. R., Illingworth, A. J. & Blackman, T. M. (1994). The use of Doppler and polarisation data to identify ground clutter and anaprop. In Proceedings of COST 75 Seminar on Advanced Radar Systems, Brussels, September 1994, European Commission, Report EUR EN,